{"id":966090,"url":"https://alion.io/job/cit-ai-engineer-master","title":"AI Engineer Master","company":{"id":957,"name":"CI&T","domain":"ciandt.com","url":"https://alion.io/company/ciandt","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":94,"open_postings":18,"ghost_share":0,"stale_share":0.444,"repost_share":0,"time_to_fill_p50_days":14,"computed_at":"2026-11-04T06:34:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Brazil"],"countries":["BR"],"hiring_countries":["BR"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anthropic","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"GraphRAG","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLMOps","optional":false},{"name":"Milvus","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Multimodal AI","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Semantic Search","optional":false},{"name":"Weaviate","optional":false},{"name":"Databricks","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"LLM","optional":true},{"name":"LoRA","optional":true},{"name":"PEFT","optional":true},{"name":"Snowflake","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-09-16T12:58:19Z","employer_posted_date":"2026-09-16","last_verified_at":"2026-11-05T02:01:22Z","board_verified":true,"closed_at":null,"days_open":49,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":48},"description":"We are looking for an experienced AI Engineer to join our Data Science & Artificial Intelligence team, designing, architecting, and building advanced AI solutions for our clients and internal teams.\nWe are looking for someone with strong hands-on experience building and deploying AI solutions in production environments, with the ability to make architectural decisions, define technical standards, and lead complex initiatives throughout the entire solution lifecycle.\nAbout the Role\nAs an AI Engineer, you will play a senior technical role, working from architecture and solution design through deployment, operation, and continuous improvement in production.\nYou will tackle complex challenges across Generative AI, intelligent agents, RAG, enterprise integrations, LLMOps, observability, and reliability, ensuring that AI solutions are scalable, secure, cost-effective, and aligned with business needs.\nWhat You'll Do\nDesign AI solution architectures, considering performance, scalability, security, technical governance, operational costs, and long-term sustainability.\n\nBuild AI-powered products and platforms, ensuring strong engineering quality and alignment with business objectives.\n\nDesign and develop intelligent agents and multi-agent systems, including planning, reasoning, task orchestration, tool usage, and integration with multiple data sources.\n\nBuild advanced Retrieval-Augmented Generation (RAG) architectures, leveraging embeddings, vector databases, GraphRAG, hybrid search, reranking, and grounding techniques.\n\nIntegrate AI solutions with enterprise ecosystems, including ERPs, CRMs, transactional systems, data platforms, and business-critical applications.\n\nEvaluate and experiment with LLMs, Small Language Models (SLMs), multimodal models, and other components of the AI technology stack.\n\nDefine metrics and AI evaluation strategies to ensure solution quality, accuracy, consistency, and reliability.\n\nImplement observability and monitoring for AI applications, tracking performance, quality, cost, usage, latency, security, and model behavior.\n\nEstablish LLMOps practices, including development, testing, versioning, deployment, and monitoring pipelines.\n\nAct as a technical reference, influencing architectural decisions, promoting engineering best practices, and contributing to the technical evolution of the AI practice.\n\nWhat We're Looking For\nBachelor's degree in Computer Science, Computer Engineering, Software Engineering, Information Systems, or a related field.\n\nAdvanced English proficiency, with the ability to communicate effectively in a global environment.\n\nStrong experience developing and deploying Generative AI solutions in production environments.\n\nExperience designing technical architectures for complex, business-critical enterprise solutions.\n\nExperience technically leading complex, multidisciplinary initiatives.\n\nAdvanced proficiency in Python.\n\nExperience with LangChain, LangGraph, Semantic Kernel, LlamaIndex, or equivalent frameworks.\n\nExperience with OpenAI, Azure OpenAI, Anthropic, Gemini, or similar AI platforms.\n\nAdvanced knowledge of RAG architectures, embeddings, and semantic search.\n\nExperience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or equivalent technologies.\n\nExperience developing APIs and microservices.\n\nExperience with Azure, AWS, or GCP.\n\nKnowledge of observability, monitoring, and reliability engineering practices.\n\nNice to Have\nExperience building AI platforms used across multiple clients, products, or business units.\n\nExperience implementing multi-agent architectures in production.\n\nKnowledge of Fine-Tuning, PEFT, LoRA, and other model customization techniques.\n\nExperience with Knowledge Graphs and GraphRAG.\n\nExperience with automated LLM evaluation and AI Evaluation frameworks.\n\nAdvanced knowledge of MLOps and LLMOps.\n\nExperience with modern data platforms such as Databricks or Snowflake.\n\nContributions to technical communities, open-source projects, or AI-related publications.\n\nAdvanced certifications in AI, cloud architecture, or software engineering.\n\nThe Challenge\nThis role goes beyond AI experimentation and proof-of-concept development. 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